Develop a Computer Vision Custom Object Detection Model
Develop a Computer Vision Custom Object Detection Model (Latest Version)


Welcome to the trial of NVIDIA AI Enterprise on NVIDIA LaunchPad.

The NVIDIA AI Enterprise suite includes the applications, frameworks, and tools that AI researchers, data scientists, and developers use for creating their AI and Machine Learning applications.


NVIDIA AI Enterprise allows AI Practitioners to run Deep Learning workflows in virtual machines with the same performance as a local workstation. AI Practitioners can quickly access Jupyter Notebooks, which leverage NVIDIA GPUs since IT Administrators have all the tools to create VMs with required NVIDIA AI Enterprise components to perform AI Training and deploy inferencing using Triton. This allows AI Practitioners to have instant access to valuable GPU resources within Enterprise data centers.

In this AI Practitioner’s LaunchPad journey, we will be creating an end to end object detection pipeline (training and deployment) without writing a single line of Tensorflow or Pytorch code! We will be using NVIDIA TAO (Train, Adapt and Optimize) Toolkit which is a low code framework that lets enterprise application developers fine-tune/train NVIDIA pretrained models with custom data to produce highly accurate computer vision, speech, and language understanding models in hours rather than months, eliminating the need for large training runs and deep AI expertise. Once we have our fine tuned object detection model, we will deploy using NVIDIA Deepstream SDK. Deepstream is a streaming analytic toolkit for building AI-powered applications. It takes the streaming data as input - from USB/CSI camera, video from file or streams over RTSP, and uses AI and computer vision to generate insights from pixels for better understanding of the environment.


This lab should take a few hours to complete. This includes the time to train the model.

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